Background Estimation with Neural AutoRegressive Flows

Description

Data driven background estimation is crucial for many scientific searches, including searches for new phenomena in experimental datasets. Neural autoregressive flows (NAF) is a deep generative model that can be used for general transformations, and is therefore attractive for this application.

The MLBENDER project focuses on studying how to develop such transformations that can be learned and applied to a region of interest.

Task ideas

Expected results

Requirements

Python, C++, and some previous experience in Machine Learning.

Mentors

Please DO NOT contact mentors directly by email, and instead please send project inquiries to ml4-sci@cern.ch with Project Title in the subject and relevant mentors will get in touch with you.

Corresponding Project

Participating Organizations